EP2727520B1 - Vorrichtung und system zum nachweis von leberfibrose - Google Patents

Vorrichtung und system zum nachweis von leberfibrose Download PDF

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EP2727520B1
EP2727520B1 EP11868676.5A EP11868676A EP2727520B1 EP 2727520 B1 EP2727520 B1 EP 2727520B1 EP 11868676 A EP11868676 A EP 11868676A EP 2727520 B1 EP2727520 B1 EP 2727520B1
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serum
classifier
hepatic fibrosis
age
serum biochemical
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French (fr)
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EP2727520A1 (de
EP2727520A4 (de
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Guanyi WANG
Yong Yang
Xinhong Wang
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INNER MONGOLIA FURUI MEDICAL SCIENCE CO Ltd
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INNER MONGOLIA FURUI MEDICAL SCIENCE CO Ltd
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Priority claimed from CN2011202228283U external-priority patent/CN202477653U/zh
Priority claimed from CN201110173535.5A external-priority patent/CN102302358B/zh
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/86—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving blood coagulating time or factors, or their receptors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/42—Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems
    • A61B5/4222—Evaluating particular parts, e.g. particular organs
    • A61B5/4244—Evaluating particular parts, e.g. particular organs liver
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/08—Hepato-biliairy disorders other than hepatitis
    • G01N2800/085—Liver diseases, e.g. portal hypertension, fibrosis, cirrhosis, bilirubin

Definitions

  • the present invention relates to the technical field of hepatic fibrosis research techniques, in particular, relates to hepatic fibrosis detection apparatus and system.
  • hepatic fibrosis and cirrhosis approximately includes the following categories: (1) Gold standard liver biopsy, i.e. hepatic fibrosis staging through pathology slide review after liver biopsy.
  • hepatitis B includes, for instance, 5 stages, namely S0, S1, S2, S3 and S4 (Chinese hepatitis B pathology scoring criteria)
  • hepatitis C includes, for instance, 5 stages, namely F0, F1, F2, F3 and F4 (Metavir score). This method is an invasive diagnostic method.
  • Serum diagnosis At present, there are more than 10 diagnostic models simulating serological variables.
  • Such models obtain mathematical formula through mathematical calculation (such as statistical regression method) according to the combinations of different serological biochemical variables.
  • Image detection such as ultrasonography, magnetic resonance (MR) imaging, and other imaging methods.
  • Ultrasonic elasticity imaging apparatus For example, FibroScan (FS) measures the stiffness value of liver, and shows different stages by different range of values. This method can also be included in the scope of the image detection;
  • genetic testing such as proteomics mapping.
  • the gold standard liver biopsy is an invasive diagnostic method. It takes a long time for the patient to recover, has safety issues, and is affected by the sample deviation. Due to the reasons such as low accuracy and sensitivity or high cost, the existing serum biochemical marker model is not widely promoted and used in clinical diagnosis.
  • the imaging method is limited by equipment.
  • the stiffness value measured by FS is not only used for hepatic fibrosis detection, but also related to corresponding liver function and lesions to a certain extent. Fibroscan is promoted and applied, but is unable to be used to detect some patients because of its restrictions.
  • the technical personnel in this field have always been striving to achieve the purpose of providing an easy-to-use and non-invasive method for diagnosis of hepatic fibrosis with high accuracy according to the actual situation.
  • the objective of the present invention is to provide a hepatic fibrosis detection apparatus and system with improved detection accuracy, sensitivity and specificity, as defined in the appended claims.
  • Another objective of the present invention is to provide a hepatic fibrosis detection apparatus, comprising: an input device used to receive age and serum bio-chemical variables, where the serum biochemical variables at least comprise blood platelet, hyaluronic acid (HA), serum direct bilirubin (DBIL), prothrombin time (PT), serum glutamic pyruvic transaminase (ALT; GPT) and serum glutamic oxaloacetic transaminase (AST; GOT); a classifier used to perform hepatic fibrosis staging or inflammation diagnosis according to the age and serum biochemical variables received by the said input device; and an output device used to output the said hepatic fibrosis staging or inflammation diagnosis results of the said classifier.
  • serum biochemical variables at least comprise blood platelet, hyaluronic acid (HA), serum direct bilirubin (DBIL), prothrombin time (PT), serum glutamic pyruvic transaminase (ALT; GPT) and serum gluta
  • the said serum biochemical variables further include serum alkaline phosphatase (ALP; AKP), serum cholinesterase (ChE) and prothrombin activity (PTA), or any one or two thereof.
  • ALP serum alkaline phosphatase
  • ChE serum cholinesterase
  • PTA prothrombin activity
  • the said serum biochemical variables also include the transforming growth factor ⁇ 1 (TGF- ⁇ 1) and ⁇ 2-macroglobulin (AMG);
  • TGF- ⁇ 1 transforming growth factor ⁇ 1
  • AMG ⁇ 2-macroglobulin
  • the classifier is also used to receive transient elastography imaging data of the liver tissue for hepatic fibrosis staging according to the said age, said serum biochemical variables and said transient elastography imaging data of the liver tissue.
  • the said classifier includes the support vector machine classifier.
  • the said support vector machine classifier is a linear support vector machine classifier or a nonlinear classifier based on kernel method.
  • the said classifier further comprises a parameter trainer used to receive the training sample data and determine the parameters of the said classifier based on the said training sample data; wherein the said training sample data include at least the said age, serum biochemical variables and corresponding hepatic fibrosis staging.
  • Training sample data may also include transient elastography imaging data.
  • the said apparatus is realized in the form of a handheld device or a floor-standing device, an on-line diagnostic system, or a stand-alone computing device.
  • the apparatus also integrates a serum biochemical variable detection apparatus and/or a transient elastography imaging apparatus.
  • It is still another object of the present invention to provide a hepatic fibrosis detection system including the above hepatic fibrosis detection apparatus and transient elastography imaging apparatus; wherein the said transient elastography imaging apparatus is used to obtain the transient elastic imaging data of the liver tissue; the said classifier receives transient elastography imaging data of the liver tissue from the said transient elastography imaging apparatus, and performs hepatic fibrosis staging according to the said age, said serum biochemical variables and said transient elastography imaging data of the liver tissue.
  • the system further comprises a serum biochemical variable detection apparatus.
  • the hepatic fibrosis detection apparatus and system in the present invention performs hepatic fibrosis staging in the light of the age and selected serum biochemical variables, and makes full use of various detection results, so that the hepatic fibrosis staging results are more accurate.
  • the hepatic fibrosis detection apparatus and system in the present invention performs hepatic fibrosis staging in the light of the age, selected serum biochemical variables and transient elastography imaging data of the liver tissue, and makes full use of various detection results, so that the hepatic fibrosis staging results are more accurate.
  • vectors are a set of various variables provided by a patient.
  • Model f is a mapping function: X ⁇ 0,1, 2, ...., n ⁇ , n may be, for instance, 3,4 or other integers. That is, if the index vector x of a patient is given, the model predicts that the pathological staging of hepatic fibrosis of this patient is f(x), the value of which is one of the n discrete values in the set ⁇ 0, 1, 2, ...., n ⁇ .
  • Specific variables and classification model are important contents of the technology. The variables and classification model used in this patent are illustrated as follows.
  • FIG 1 shows the structural diagram of a first embodiment of the hepatic fibrosis detection apparatus according to the present invention.
  • the hepatic fibrosis detection apparatus in this embodiment comprises an input device 11, a classifier 12 and an output device 13.
  • the input device 11 is used to receive age and serum biochemical variables
  • the serum biochemical variables include at least the blood platelet, hyaluronic acid (HA), serum direct bilirubin (DBIL), prothrombin time (PT), serum glutamic pyruvic transaminase (ALT; GPT) and serum glutamic oxaloacetic transaminase (AST; GOT).
  • the classifier 12 performs hepatic fibrosis staging according to the age and serum biochemical variables received by the input device 11, and sends the hepatic fibrosis staging result to the output device 13.
  • the classifier 12 obtains the ratio introduced by two experts: serum glutamic oxaloacetic transaminase (AST; GOT)/blood platelet and serum glutamic oxaloacetic transaminase (AST; GOT)/serum glutamic pyruvic transaminase (ALT; GPT), which are used to replace the serum glutamic oxaloacetic transaminase (AST; GOT) and serum glutamic pyruvic transaminase (ALT; GPT) as input parameters of the classifier.
  • the output device 13 outputs the hepatic fibrosis staging results of the classifier 12.
  • the classifier 12 may be a support vector machine classifier, a classifier based on the decision maker model, a support vector regression model classifier, a logistic regression classifier, an Adaboost ensemble classifier, or a PCA (principal component analysis)+KNN (K nearest neighbor) model classifier.
  • the classifier 12 may be realized on a computing device through software, or be realized through special hardware, circuit or device.
  • the classifier can be used to obtain more accurate hepatic fibrosis detection effect through age and selected serum biochemical variables than the detection method of the prior art.
  • Detection of the serum biochemical variables such as blood platelet, hyaluronic acid (HA), serum direct bilirubin (DBIL), prothrombin time (PT), serum glutamic pyruvic transaminase (ALT; GPT) and serum glutamic oxaloacetic transaminase (AST; GOT) is more popularized, and can be achieved in general hospitals. Therefore, the application and popularization of the scheme can be expanded, so as to reduce the overall cost and difficulty of the detection.
  • different classifiers may be selected according to the actual needs, thereby increasing the accuracy of the classifier in practice.
  • the hepatic fibrosis detection apparatus in the present invention may be realized in multiple forms according to the clinical needs.
  • the input device, classifier and output device are arranged in a computer, the input device and the output device correspond to the input equipment such as the computer keyboard, touch screen, mouse and device interface, and the output equipment such as the display screen, audio output device and output interface etc.;
  • the classifier can be realized through software, or be realized through special classifier circuit connected to the motherboard.
  • This detection apparatus can be achieved through a computer, and its implementation cost can be reduced by making full use of the characteristics of high popularization rate of computers.
  • the input device, classifier and output device are arranged in the same portable handheld device, which may be a general handheld computer, or a special device for diagnosis of hepatic fibrosis.
  • the detection apparatus is realized in the form of a handheld device, which improves the convenience and flexibility for use of the device.
  • the hepatic fibrosis detection apparatus can also be achieved in the form of an online diagnostic system. A specific embodiment of an online diagnostic system is illustrated below with reference to Figure 2 .
  • the serum biochemical variables further include serum alkaline phosphatase (ALP; AKP), serum cholinesterase (ChE) and prothrombin activity (PTA), or any one or two of the above 3 variables.
  • ALP serum alkaline phosphatase
  • ChE serum cholinesterase
  • PTA prothrombin activity
  • the serum biochemical variables also include the transforming growth factor ⁇ 1 (TGF- ⁇ 1) and ⁇ 2-macroglobulin (AMG); the classifier is used for hepatic fibrosis staging according to the received age and serum biochemical variables of the input device.
  • TGF- ⁇ 1 transforming growth factor ⁇ 1
  • AMG ⁇ 2-macroglobulin
  • the classifier can be used to obtain more accurate hepatic fibrosis detection effect through age and selected serum biochemical variables than the detection method of the prior art.
  • Figure 2 shows the structural diagram of a second embodiment of the hepatic fibrosis detection apparatus according to the present invention.
  • the input device 21 may be a computer, a tablet PC, or a PDA, etc.
  • Equipment as the input device may be connected to the classifier 22 through wire connection or wireless connection etc.
  • the classifier 22 may be a server, a computer or special equipment.
  • the hepatic fibrosis staging result output by the classifier 22 may be output through the output device 23, or be output to the users through the input device 21.
  • the detection apparatus can be realized in the form of an online diagnostic system only by a classifier in the background, which may include a plurality of input terminals and output terminals, so as to achieve detection support by more diagnosis sectors, and reduce the unit detection cost.
  • the input data of the classifier may include not only age and serum biochemical variables mentioned in above embodiment, but also transient hepatic elasticity imaging data of the liver tissue, namely the liver tissue stiffness values obtained through the transient elastography imaging apparatus.
  • FIG. 3 shows the structural diagram of a third embodiment of the hepatic fibrosis detection system according to the present invention.
  • hepatic fibrosis detection system in this embodiment comprises an input device 31, a classifier 32, an output device 33 and a transient elastography imaging apparatus 34. Please refer to the description of the above embodiments for the input device 31 and output device 33, which are not illustrated in detail here for simplicity.
  • Transient elastography imaging apparatus 34 can be used to obtain transient elastography imaging data of the liver tissue; the classifier 33 receives transient elastography imaging data of the liver tissue from the transient elastography imaging apparatus 34, and performs hepatic fibrosis staging based on age, serum biochemical variables and transient elastography imaging data of the liver tissue.
  • Transient elastography imaging apparatus 34 FibroScan for instance, can be used to obtain FibroScan stiffness value of the liver tissue.
  • the system performs hepatic fibrosis staging in the light of the age, selected serum biochemical variables and transient elastography imaging data of the liver tissue, and makes full use of various detection results, so that the hepatic fibrosis staging results are more accurate.
  • the system further comprises a serum biochemical variable detection apparatus, which is used to detect the samples in the kit, obtain the data of serum biochemical variables, and send such data to the classifier through the input device.
  • a serum biochemical variable detection apparatus which is used to detect the samples in the kit, obtain the data of serum biochemical variables, and send such data to the classifier through the input device.
  • FIG 4 shows the schematic view of an embodiment of the transient elastography imaging apparatus and the probe thereof.
  • the elasticity imaging apparatus 44 comprises a probe socket 441, which is used to connect with an ultrasound probe 45, and further comprises a data transmission interface 442, which is used to connect with a computer or network for data transfer.
  • Ultrasound probe 45 comprises an ultrasound transducer 443, a switch button 444, an electrodynamics transducer 445, a connection cable 446 and a jack 447.
  • Bagging method may be used: train a plurality of independent classifiers, and obtain the final classification result through voting as per the results of a plurality of classifiers.
  • this Bagging method uses a method similar to cross-validation, randomly divides the samples into n aliquots each time, trains the classifier with n-1 portions thereof (parameters are also determined through the grid search method at this time), and predicts according to the remaining portion.
  • FIG. 5 shows the structural diagram of a fourth embodiment of the hepatic fibrosis detection system according to the present invention.
  • the hepatic fibrosis detection system in this embodiment comprises an input device 31, a classifier 52, an output device 33 and a transient elastography imaging apparatus 34.
  • the input device 31, output device 33 and transient elastography imaging apparatus 34 can be found in the description of the above embodiments, and are not illustrated in detail here for simplicity.
  • the classifier 52 comprises a voting machine 523, and two or more sub-classifiers such as the first sub-classifier 521, the second sub-classifier 522, and so on.
  • Each sub-classifier 521, 522, etc. obtains their respective hepatic fibrosis staging results according to the age, serum biochemical variables and transient elastography imaging data of the liver tissue, and outputs their hepatic fibrosis staging results to the voting machine 523.
  • the voting machine 523 determines the output hepatic fibrosis staging results according to the hepatic fibrosis staging results of each sub-classifier in the form of voting, for instance.
  • FIG. 6 shows the structural diagram of a fifth embodiment of the hepatic fibrosis detection apparatus according to the present invention.
  • the hepatic fibrosis detection apparatus in this embodiment comprises an input device 31, a classifier 32, an output device 33, a transient elastography imaging apparatus 34 and a parameter trainer 65.
  • the parameter trainer 65 receives the training sample data, and determines the classifier parameters according to the training sample data; wherein, the training sample data may include age, serum biochemical variables and corresponding hepatic fibrosis staging; Or, the training sample data may include age, serum biochemical variables, transient elastography imaging data of the liver tissue and corresponding hepatic fibrosis staging.
  • the hepatic fibrosis classification model can be obtained through training. Taking into account that the sample may be unceasingly enriched, therefore, a self-learning strategy of the model is designed.
  • the learning strategy of the above model is completely compiled to an automated training process, the input interface is the sample set; and the output interface is the finally used prediction function. Therefore, once the sample set is updated, it is only necessary to adopt automatic training function of the program, so that the self-learning process of the model can be completed. Meanwhile, the old model will also be backed up and saved accordingly, so as to deal with the model restoration work under unexpected conditions.
  • the classification model is introduced in the light of specific examples of support vector machines as follows. The training strategy of this classification model will be illustrated in detail below; relevant eigenvectors, if any, will be uniformly expressed as the vector ⁇ .
  • sub-problem 1 means to determine whether the stiffness value of a given sample is greater than, equal to, or less than 1. This also applies to the remaining sub-problems.
  • the sub-problems can be combined into the final decision making rules.
  • the results predicted with four sub-models are a sequence ( f1, f2, f3, f4 ), every element in the sequence is 0 or 1, so there are a total of 16 possible values of the sequence.
  • the decision is made according to the final prediction results corresponding to each value and the rules in Table 2.
  • SVM Support Vector Machine
  • each model is divided into four sub-models, and each sub-model is a binary classification problem.
  • the support vector machine is used as the basic classifier in this invention.
  • the support vector machine is an excellent classification model, which classifies the samples in the sample space according to the classification margin maximization principle, and ensures better generalization performance (the ability to predict unknown samples) on the premise of obtaining lower training error rate.
  • Figure 7 shows the schematic view of a linearly separable SVM classifier.
  • Figure 7 Schematic view of a maximum margin SVM classification hyperplane. Solid points and hollow points represent two types of sample points.
  • the classification hyperplane of the intermediate solid line has larger classification margin than all remaining classification hyperplanes of dotted line, and has better generalization performance as a consequence.
  • SVM is a linear classifier.
  • C is a parameter weighing the training error rate and generalization performance, and is usually determined through cross-validation.
  • SVM can also learn a nonlinear model. It maps a sample from the original space into a higher dimensional feature space using the kernel method and a specific non-linear mapping, so that the linearly inseparable data in the original space can be linearly separable in the high-dimensional space.
  • a linear model is designed in the high-dimensional space, and it is equivalent to a nonlinear model designed in the original space.
  • Figure 8 shows a schematic view of improving a two-dimensional sample to a three-dimensional space through the polynomial kernel function, so that the original inseparable samples are linearly separable in a high-dimensional space.
  • Figure 8 shows the schematic view of the nonlinear SVM algorithm.
  • the original method is improved to a high-dimensional space using the kernel method, so that it is linearly separable in the high-dimensional space, which is equivalent to being nonlinearly separable in the original space.
  • kernel function needs to satisfy Mercer conditions. There are three frequently seen kernel functions:
  • the nonlinear SVM can be used as the most basic classifier, and the Gaussian kernel is selected as the kernel.
  • the above characteristics 5 and 8 are 2 specific value characteristics introduced according to the expert advice. They are related to three characteristics 2, 14 and 15.
  • the characteristic 2 is provided in the above table, and the characteristics 14 and 15 are as follows: Table 4 Characteri stic number Medical name Remarks 14 Serum glutamic oxaloacetic transaminase (AST; GOT) Serum biochemical variable 15 Serum glutamic pyruvic transaminase (ALT; GPT) Serum biochemical variable Table 5 Age and serum biochemical variable 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 Age, serum biochemical variable and FibroScan stiffness value 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13 Age and serum biochemical variable 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 Age, serum biochemical variable and FibroScan stiffness value 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 Table 6 Age and serum biochemical variable 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 Age, serum biochemical variable and FibroScan stiffness value 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 Age
  • the serum biochemical variables include the blood platelet, hyaluronic acid (HA), serum direct bilirubin (DBIL), prothrombin time (PT), serum glutamic pyruvic transaminase (ALT; GPT), serum glutamic oxaloacetic transaminase (AST; GOT), transforming growth factor ⁇ 1 (TGF- ⁇ 1), and ⁇ 2-macroglobulin (AMG).
  • the cost required to obtain them is different.
  • a classification model is designed based on medical indicators such as serum biochemical variables and FibroScan variables according to the "gold standard", so as to non-invasively predict hepatic fibrosis staging.
  • Eigenvectors of the patient condition are obtained through test of specific biochemical variables of the patients. Based on the eigenvectors, the model predicts current pathological staging S0-S4 (or F0-F4) of the patients (The higher the level is, the more severe the hepatic fibrosis is) .
  • the technical solution in the present invention is selected from a plurality of parameters, mainly including: gender, age, HBV DNA level, a variety of liver enzyme variables, related cholesterol, and almost all biochemical variables, special detection index of hepatic fibrosis, FibroScan stiffness value and so on.
  • n serum biochemical variables with the best correlation with hepatic fibrosis are ultimately determined for clinical diagnosis, and the model for diagnosis of hepatic fibrosis and hepatic cirrhosis is obtained in combination with the FS detection result.
  • the model is also divided into two versions, in order to facilitate detection in different hospitals.
  • the hepatic fibrosis detection system in the embodiments of the present invention is characterized by noninvasiveness, strong practicability, simple method, low price and good security etc.:

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Claims (6)

  1. Gerät zum Feststellen von Leberfibrose, dadurch gekennzeichnet, dass es umfasst:
    eine Eingabevorrichtung (11, 31), die zum Empfangen des Alters, der biochemischen Serumvariablen und der transienten, bildgebenden Elastografiedaten verwendet ist, wobei die biochemischen Serumvariablen wenigstens Blutplättchen, Hyaluronsäure (HA), direktes Serumbilirubin (DBIL), eine Prothrombinzeit (PT), Serum-Glutamat-Pyruvat-Transaminase (ALT; GPT), Serum-Glutamat-Oxalacetat-Transaminase (AST; GOT), alkaline Serum-Phosphatase (ALP; AKP), Serum-Cholinesterase (ChE), eine Prothrombin-Aktivität (PTA), einen transformierenden Wachstumsfaktor β1 (TGF-β1) und α2-Makroglobulin (AMG) einschließen und wobei die transienten, bildgebenden Elastografiedaten wenigstens einen FibroScan-Steifigkeitswert einschließen;
    einen Klassierer (12, 32, 52), der zum Bereitstellen von Leberfibrose oder zur Diagnose einer Entzündung nach dem Alter verwendet ist, wobei die genannten biochemischen Serumvariablen und die genannten transienten, bildgebenden Elastografiedaten durch die Eingabevorrichtung (11, 31) empfangen sind, wobei der Klassierer (12, 32, 52) ein linearer Trägervektor-Maschinenklassierer (12, 32, 52) oder ein nicht linearer Trägervektor-Maschinenklassierer (12, 32, 52) basierend auf dem Kernel-Verfahren ist;
    eine Ausgabevorrichtung (13, 33), die zum Ausgeben der genannten Bereitstellung von Leberfibrose oder von Ergebnissen der Diagnose einer Entzündung des genannten Klassierers (12, 32, 52) verwendet ist.
  2. Gerät gemäß Definition in Anspruch 1, dadurch gekennzeichnet, dass der genannte Klassierer (12, 32, 52) wenigstens zwei unterschiedliche Klassierer (12, 32, 52) umfasst und die Bereitstellung von Leberfibrose gemäß den Ergebnissen von wenigstens zwei der obigen unterschiedlichen Klassierern (12, 32, 52) erhält.
  3. Gerät gemäß Definition in Anspruch 1 - 2, dadurch gekennzeichnet, dass das Gerät weiterhin einen Parameter-Trainer (65) umfasst, der zum Empfangen von Schulungs-Beispieldaten verwendet ist und die Parameter des genannten Klassierers (12, 32, 52) basierend auf den genannten Schulungs-Beispieldaten bestimmt; wobei die genannten Schulungs-Beispieldaten wenigstens das genannte Alter, die biochemischen Serumvariablen, die transienten, bildgebenden Elastografiedaten und die entsprechende Bereitstellung von Leberfibrose einschließen.
  4. Gerät gemäß Definition in Anspruch 1 bis 3, dadurch gekennzeichnet, dass das genannte Gerät in Form von einer tragbaren Vorrichtung, eines Online-Diagnosesystems oder einer eigenständigen Berechnungsvorrichtung realisiert ist.
  5. System zum Feststellen von Leberfibrose, dadurch gekennzeichnet, dass das System das Gerät zum Feststellen von Leberfibrose, das in irgendeinem der Ansprüche 1 bis 4 definiert ist, und ein transientes, bildgebendes Elastografiegerät (34) einschließt; wobei das genannte transiente, bildgebende, Elastografiegerät (34) verwendet ist, um transiente, bildgebende Elastografiedaten des Lebergewebes zu erhalten; der genannte Klassierer (12, 32, 52) transiente, bildgebende Elastografiedaten des Lebergewebes von dem transienten, bildgebenden Elastografiegerät (34) empfängt und die Bereitstellung von Leberfibrose nach dem genannten Alter, den genannten biochemischen Serumvariablen und die genannten transienten, bildgebenden Elastografiedaten des Lebergewebes durchführt.
  6. System gemäß Definition in Anspruch 5, dadurch gekennzeichnet, dass das System weiterhin ein Gerät zum biochemischen, variablen Feststellen von Serum umfasst, das genannte Gerät zum biochemischen, variablen Feststellen von Serum an die genannte Eingabevorrichtung (11, 31) angeschlossen ist und die genannten biochemischen Serumvariablen durch die Eingabevorrichtung (11, 31) zum Klassierer (12, 32, 52) sendet.
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